/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#include "tensorflow/compiler/mlir/tensorflow/utils/call_graph_util.h"

#include "llvm/Support/raw_ostream.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/IR/BuiltinOps.h"  // from @llvm-project
#include "mlir/IR/DialectRegistry.h"  // from @llvm-project
#include "mlir/IR/Location.h"  // from @llvm-project
#include "mlir/IR/MLIRContext.h"  // from @llvm-project
#include "mlir/IR/OwningOpRef.h"  // from @llvm-project
#include "mlir/Parser/Parser.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/tensorflow/dialect_registration.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/attribute_utils.h"
#include "tensorflow/core/lib/core/status_test_util.h"
#include "tensorflow/core/platform/test.h"

namespace tensorflow {
namespace {

TEST(CallGraphUtilTest, GetEntryFunctions) {
  const char *const code = R"mlir(
func.func @entry_func_1(%arg0: tensor<i32>) -> tensor<i32> attributes {tf.entry_function = {}} {
  %0 = "tf.StatefulPartitionedCall"(%arg0) {_xla_compile_device_type = "CPU", config = "", config_proto = "", device = "/device:CPU:0", executor_type = "", f = @func} : (tensor<i32>) -> (tensor<i32>)
  func.return %0 : tensor<i32>
}

func.func @entry_func_2(%arg0: tensor<i32>) -> tensor<i32> attributes {tf_saved_model.initializer_type = ""} {
  %0 = "tf.StatefulPartitionedCall"(%arg0) {_xla_compile_device_type = "CPU", config = "", config_proto = "", device = "/device:CPU:0", executor_type = "", f = @func} : (tensor<i32>) -> (tensor<i32>)
  func.return %0 : tensor<i32>
}

func.func @func(%arg0: tensor<i32>) -> tensor<i32> {
  func.return %arg0 : tensor<i32>
}
)mlir";
  mlir::MLIRContext context;
  context.loadDialect<mlir::func::FuncDialect, mlir::TF::TensorFlowDialect>();
  mlir::OwningOpRef<mlir::ModuleOp> module =
      mlir::parseSourceString<mlir::ModuleOp>(code, &context);
  ASSERT_TRUE(module);
  auto entry_funcs = GetEntryFunctions(*module);
  EXPECT_EQ(entry_funcs.size(), 2);
  EXPECT_EQ(entry_funcs[0].getSymName(), "entry_func_1");
  EXPECT_EQ(entry_funcs[1].getSymName(), "entry_func_2");
}

TEST(CallGraphUtilTest, GetCallees) {
  const char *const code = R"mlir(
func.func @entry_func(%arg0: tensor<i32>) -> tensor<i32> attributes {tf_saved_model.initializer_type = ""} {
  %0 = "tf.While"(%arg0) {cond = @while_cond_func, body = @while_body_func, is_stateless = true} : (tensor<i32>) -> (tensor<i32>)
  func.return %0 : tensor<i32>
}

func.func @while_cond_func(%arg0: tensor<i32>) -> tensor<i1> {
  %0 = "tf.Const"() {value = dense<0> : tensor<i1>} : () -> tensor<i1>
  func.return %0 : tensor<i1>
}

func.func @while_body_func(%arg0: tensor<i32>) -> (tensor<i32>) {
  %0 = "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>
  func.return %0 : tensor<i32>
}


)mlir";
  mlir::MLIRContext context;
  context.loadDialect<mlir::func::FuncDialect, mlir::TF::TensorFlowDialect>();
  mlir::OwningOpRef<mlir::ModuleOp> module =
      mlir::parseSourceString<mlir::ModuleOp>(code, &context);
  ASSERT_TRUE(module);
  mlir::SymbolTable symtab(*module);
  llvm::SmallVector<mlir::func::FuncOp> callees;
  module->walk([&](mlir::SymbolUserOpInterface op) {
    auto result = GetCallees(op, symtab, callees).succeeded();
    ASSERT_TRUE(result);
    EXPECT_EQ(callees.size(), 2);
    EXPECT_EQ(callees[0].getSymName(), "while_body_func");
    EXPECT_EQ(callees[1].getSymName(), "while_cond_func");
  });
}

TEST(CallGraphUtilTest, GetFirstOpsOfType) {
  const char *const code = R"mlir(
func.func @entry_func(%arg0: tensor<i32>) -> tensor<i32> attributes {tf.entry_function = {}} {
  %0 = "tf.While"(%arg0) {cond = @while_cond_func, body = @while_body_func, is_stateless = true} : (tensor<i32>) -> (tensor<i32>)
  func.return %0 : tensor<i32>
}

func.func @while_cond_func(%arg0: tensor<i32>) -> tensor<i1> {
  %0 = "tf.Const"() {value = dense<0> : tensor<i1>} : () -> tensor<i1>
  func.return %0 : tensor<i1>
}

// CHECK-LABEL: func.func @while_body_func
func.func @while_body_func(%arg0: tensor<i32>) -> (tensor<i32>) {
  %0 = "tf.StatefulPartitionedCall"(%arg0) {config = "", config_proto = "", device = "/device:CPU:0", executor_type = "", f = @outer_stateful_pcall_func} : (tensor<i32>) -> (tensor<i32>)
  func.return %0 : tensor<i32>
}

func.func @outer_stateful_pcall_func(%arg0: tensor<i32>) -> (tensor<i32>) {
  %0 = "tf.StatefulPartitionedCall"(%arg0) {_xla_compile_device_type = "CPU", config = "", config_proto = "", device = "/device:CPU:0", executor_type = "", f = @inner_stateful_pcall_func} : (tensor<i32>) -> (tensor<i32>)
  func.return %0 : tensor<i32>
}

func.func @inner_stateful_pcall_func(%arg0: tensor<i32>) -> tensor<i32> {
  %0 = "tf.StatefulPartitionedCall"(%arg0) {_xla_compile_device_type = "CPU", config = "", config_proto = "", device = "/device:CPU:0", executor_type = "", f = @func} : (tensor<i32>) -> (tensor<i32>)
  func.return %0 : tensor<i32>
}

func.func @func(%arg0: tensor<i32>) -> tensor<i32> {
  func.return %arg0 : tensor<i32>
}
)mlir";
  auto has_compile_device_type = [](mlir::SymbolUserOpInterface op) {
    return op->hasAttr(tensorflow::kCompileDeviceTypeAttr);
  };
  mlir::MLIRContext context;
  context.loadDialect<mlir::func::FuncDialect, mlir::TF::TensorFlowDialect>();
  mlir::OwningOpRef<mlir::ModuleOp> module =
      mlir::parseSourceString<mlir::ModuleOp>(code, &context);
  ASSERT_TRUE(module);
  mlir::SymbolTable symtab(*module);
  llvm::SmallVector<mlir::func::FuncOp> entry_funcs =
      GetEntryFunctions(*module);
  EXPECT_EQ(entry_funcs.size(), 1);
  EXPECT_EQ(entry_funcs[0].getSymName(), "entry_func");
  llvm::SmallVector<mlir::SymbolUserOpInterface> outermost_pcall_ops;
  auto result =
      mlir::GetFirstOpsOfType<mlir::TF::StatefulPartitionedCallOp,
                              mlir::TF::PartitionedCallOp>(
          entry_funcs[0], symtab, has_compile_device_type, outermost_pcall_ops)
          .succeeded();
  ASSERT_TRUE(result);
  EXPECT_EQ(outermost_pcall_ops.size(), 1);
  auto func =
      llvm::dyn_cast<mlir::func::FuncOp>(outermost_pcall_ops[0]->getParentOp());
  ASSERT_TRUE(func);
  EXPECT_EQ(func.getSymName(), "outer_stateful_pcall_func");
}

TEST(CallGraphUtilTest, GetOpsOfTypeUntilMiss) {
  const char *const code = R"mlir(
func.func @entry_func(%arg0: tensor<i32>) -> tensor<i32> attributes {tf.entry_function = {}} {
  %0 = "tf.While"(%arg0) {cond = @while_cond_func, body = @while_body_func, is_stateless = true} : (tensor<i32>) -> (tensor<i32>)
  func.return %0 : tensor<i32>
}

func.func @while_cond_func(%arg0: tensor<i32>) -> tensor<i1> {
  %0 = "tf.Const"() {value = dense<0> : tensor<i1>} : () -> tensor<i1>
  func.return %0 : tensor<i1>
}

// CHECK-LABEL: func.func @while_body_func
func.func @while_body_func(%arg0: tensor<i32>) -> (tensor<i32>) {
  %0 = "tf.StatefulPartitionedCall"(%arg0) {config = "", config_proto = "", device = "/device:CPU:0", executor_type = "", f = @outer_stateful_pcall_func} : (tensor<i32>) -> (tensor<i32>)
  func.return %0 : tensor<i32>
}

func.func @outer_stateful_pcall_func(%arg0: tensor<i32>) -> (tensor<i32>) {
  %0 = "tf.StatefulPartitionedCall"(%arg0) {config = "", config_proto = "", device = "/device:CPU:0", executor_type = "", f = @inner_stateful_pcall_func} : (tensor<i32>) -> (tensor<i32>)
  func.return %0 : tensor<i32>
}

func.func @inner_stateful_pcall_func(%arg0: tensor<i32>) -> tensor<i32> {
  %0 = "tf.StatefulPartitionedCall"(%arg0) {_xla_compile_device_type = "CPU", config = "", config_proto = "", device = "/device:CPU:0", executor_type = "", f = @func} : (tensor<i32>) -> (tensor<i32>)
  func.return %0 : tensor<i32>
}

func.func @func(%arg0: tensor<i32>) -> tensor<i32> {
  func.return %arg0 : tensor<i32>
}
)mlir";
  auto has_no_compile_device_type = [](mlir::SymbolUserOpInterface op) {
    return !op->hasAttr(tensorflow::kCompileDeviceTypeAttr);
  };
  mlir::MLIRContext context;
  context.loadDialect<mlir::func::FuncDialect, mlir::TF::TensorFlowDialect>();
  mlir::OwningOpRef<mlir::ModuleOp> module =
      mlir::parseSourceString<mlir::ModuleOp>(code, &context);
  ASSERT_TRUE(module);
  mlir::SymbolTable symtab(*module);
  llvm::SmallVector<mlir::func::FuncOp> entry_funcs =
      GetEntryFunctions(*module);
  EXPECT_EQ(entry_funcs.size(), 1);
  EXPECT_EQ(entry_funcs[0].getSymName(), "entry_func");
  llvm::SmallVector<mlir::SymbolUserOpInterface> noinline_pcall_ops,
      outermost_pcall_ops;
  auto result =
      mlir::GetOpsOfTypeUntilMiss<mlir::TF::StatefulPartitionedCallOp,
                                  mlir::TF::PartitionedCallOp>(
          entry_funcs[0], symtab, has_no_compile_device_type,
          /*hits*/ noinline_pcall_ops, /*first_misses*/ outermost_pcall_ops)
          .succeeded();
  ASSERT_TRUE(result);
  EXPECT_EQ(noinline_pcall_ops.size(), 2);
  auto func =
      llvm::dyn_cast<mlir::func::FuncOp>(noinline_pcall_ops[0]->getParentOp());
  ASSERT_TRUE(func);
  EXPECT_EQ(func.getSymName(), "while_body_func");
  func =
      llvm::dyn_cast<mlir::func::FuncOp>(noinline_pcall_ops[1]->getParentOp());
  ASSERT_TRUE(func);
  EXPECT_EQ(func.getSymName(), "outer_stateful_pcall_func");

  EXPECT_EQ(outermost_pcall_ops.size(), 1);
  func =
      llvm::dyn_cast<mlir::func::FuncOp>(outermost_pcall_ops[0]->getParentOp());
  ASSERT_TRUE(func);
  EXPECT_EQ(func.getSymName(), "inner_stateful_pcall_func");
}

}  // namespace
}  // namespace tensorflow
